AIMC Topic: Sensitivity and Specificity
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[Artificial intelligence for eye care].
Nederlands tijdschrift voor geneeskunde
Sep 17, 2020
Technological developments in ophthalmic imaging and artificial intelligence (AI) create new possibilities for diagnostics in eye care. AI has already been applied in ophthalmic diabetes care. AI-systems currently detect diabetic retinopathy in gener...
Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up.
Frontiers in endocrinology
Sep 16, 2020
Some patients with acromegaly do not reach the remission standard in the short term after surgery but achieve remission without additional postoperative treatment during long-term follow-up; this phenomenon is defined as postoperative delayed remiss...
Review of medical image recognition technologies to detect melanomas using neural networks.
BMC bioinformatics
Sep 14, 2020
BACKGROUND: Melanoma is one of the most aggressive types of cancer that has become a world-class problem. According to the World Health Organization estimates, 132,000 cases of the disease and 66,000 deaths from malignant melanoma and other forms of ...
Application of hierarchical clustering to multi-parametric MR in prostate: Differentiation of tumor and normal tissue with high accuracy.
Magnetic resonance imaging
Sep 12, 2020
PURPOSE: Hierarchical clustering (HC), an unsupervised machine learning (ML) technique, was applied to multi-parametric MR (mp-MR) for prostate cancer (PCa). The aim of this study is to demonstrate HC can diagnose PCa in a straightforward interpretab...
Efficacy of endoscopic ultrasound with artificial intelligence for the diagnosis of gastrointestinal stromal tumors.
Journal of gastroenterology
Sep 11, 2020
BACKGROUND: Although endoscopic ultrasound (EUS) is reported to be suitable for determining the layer from which subepithelial lesions (SELs) originate, it is difficult to distinguish gastrointestinal stromal tumor (GIST) from non-GIST using only EUS...
Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain.
Scandinavian journal of trauma, resuscitation and emergency medicine
Sep 11, 2020
BACKGROUND: A big-data-driven and artificial intelligence (AI) with machine learning (ML) approach has never been integrated with the hospital information system (HIS) for predicting major adverse cardiac events (MACE) in patients with chest pain in ...
How Good Is Machine Learning in Predicting All-Cause 30-Day Hospital Readmission? Evidence From Administrative Data.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Sep 7, 2020
OBJECTIVES: Hospital readmission is a main cost driver for healthcare systems, but existing works often had poor or moderate predictive results. Although the available information differs in different studies, improving prediction is different from t...
Multi-slice representational learning of convolutional neural network for Alzheimer's disease classification using positron emission tomography.
Biomedical engineering online
Sep 7, 2020
BACKGROUND: Alzheimer's Disease (AD) is a degenerative brain disorder that often occurs in people over 65 years old. As advanced AD is difficult to manage, accurate diagnosis of the disorder is critical. Previous studies have revealed effective deep ...
Image-level detection of arterial occlusions in 4D-CTA of acute stroke patients using deep learning.
Medical image analysis
Sep 5, 2020
The triage of acute stroke patients is increasingly dependent on four-dimensional CTA (4D-CTA) imaging. In this work, we present a convolutional neural network (CNN) for image-level detection of intracranial anterior circulation artery occlusions in ...
A deep learning approach in diagnosing fungal keratitis based on corneal photographs.
Scientific reports
Sep 2, 2020
Fungal keratitis (FK) is the most devastating and vision-threatening microbial keratitis, but clinical diagnosis a great challenge. This study aimed to develop and verify a deep learning (DL)-based corneal photograph model for diagnosing FK. Corneal ...
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